Learn Hadoop Online

Hadoop Online training course is designed to enhance your knowledge and skills to
become a successful Hadoop developer and In-depth knowledge of core concepts
will be covered in the course along with implementation on varied industry use-cases.
take a look on HADOOP ADMIN AND DEVELOPER COURSE content
Skype Id: info.vibloo Email: info@Vibloo.com USA: +1-248-809-1418 IND: +91-40-3296-5222

 What is Hadoop?
 The Hadoop Distributed File System
 Hadoop Map Reduce Works
 Anatomy of a Hadoop Cluster
 Master Daemons
 Name node
Introduction to Hadoop
 Job Tracker
 Secondary name node
 Slave Daemons
 Job tracker
 Task tracker
www.vibloo.com/Hadoop-Online-Training

 Blocks and Splits
 Input and HDFS Splits
 Data Replication
 Hadoop Rack Aware
 Data high availability
 Data Integrity
 Cluster architecture and block placement
 Accessing HDFS
 JAVA & CLI Approach
HDFS (Hadoop Distributed File System)
 Programming Practices
 Developing MapReduce Programs in
 Running without HDFS and MapReduce
 Running all daemons in a single node
 Running daemons on dedicated nodes
 Local Mode
 Pseudo-distributed Mode
 Fully distributed mode

 Make a fully distributed Hadoop cluster on a single laptop/desktop
 Name Node in Safe mode
 Meta Data Backup
 Integrating Kerberos security in hadoop
Setup Hadoop cluster of Apache, Cloudera and Horton Works

 Examining a Sample MapReduce Program, with several examples
 Basic API Concepts
 The Driver Code
 The Mapper
 The Reducer
 Hadoop's Streaming API
Writing a MapReduce Program

 The configure and close Methods
 Sequence Files
 Record Reader
 Record Writer
 Role of Reporter
 Output Collector
Performing several hadoop jobs
 Processing XML files
 Counters
 Directly Accessing HDFS
 Tool Runner
 Using The Distributed Cache

 Sorting and Searching
 Indexing
 Classification/Machine Learning
 Term Frequency - Inverse Document Frequency
 Word Co-Occurrence
Common MapReduce Algorithms
 Creating an Inverted Index
 Identity Mapper
 Identity Reducer
 MapReduce applications

 Testing with MRUnit
 Logging
 Other Debugging Strategies
Debugging MapReduce Programs

 A Recap of the MapReduce Flow
 The Secondary Sort
 Customized Input Formats and Output Formats
Advanced MapReduce Programming

 Counters
 Skipping Bad Records
 Rerunning failed tasks with Isolation Runner
Monitoring and debugging on a Production Cluster

 Reducing network traffic with combiner
 Partitioners
 Using Compression
 Reusing the JVM
 Running with speculative execution
 Refactoring code and rewriting algorithms Parameters affecting Performance
 Other Performance Aspects
Tuning for Performance in MapReduce

HBase
 HBase concepts
 HBase architecture
 Region server architecture
 File storage architecture
 HBase basics
 Column access
 Scans
 HBase use cases
 Install and configure HBase on a multi node cluster
 Create database
 Develop and run sample applications
 Access data stored in HBase using clients
like Java, Python and Pearl
 HBase and Hive Integration
 HBase admin tasks
 Defining Schema and basic operation

PIG
 Pig basics
 Install and configure PIG on a cluster
 PIG Vs MapReduce and SQL
 Pig Vs Hive
 Write sample Pig Latin scripts
 Modes of running PIG
 Running in Grunt shell
 Programming in Eclipse
 Running as Java program
 PIG UDFs
 Pig Macros

Flume, Chukwa, Avro, Scribe, Thrift
 Flume and Chukwa concepts
 Use cases of Thrift
 Avro and scribe
 Install and configure flume on cluster
 Create a sample application to capture logs from Apache using flume

CDH4 Enhancements
 Name Node High – Availability
 Name Node federation
 Fencing
 YARN

Hadoop Challenges
 Hadoop disaster recovery
 Hadoop suitable cases

Learn Hadoop Online

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Learn Hadoop Online